Abstract
Web as a platform to integrate applications, encapsulated as web services and composed using semantic technologies, is well established. However, in many domains, there are far more applications whose description or implementation are available than those using service-enabled access. For example, in the environmental domain, analytical models (computational functions) are crucial to analyze collected sensor data, extrapolate them for uncovered but interesting settings of interest, and gain insights for action. However, using a particular analytical model may be appropriate under very specific conditions - terrain of region, weather conditions, pollutants, types of pollutant sources, data sampling rate, etc. Thus, finding a relevant analytical model for a given setting is of particular interest to environmental agencies around the world. But it is also a complex activity since there are hundreds of analytical models with numerous constraints. In this paper, we present SemEnAl, an approach to use semantic annotations derived from a domain ontology to address this discovery problem and demonstrate its effectiveness. Our experiments with search system for analytical models indicates that using SemEnAl can provide high precision, to an extent of about 100% relevant models for selected queries. The paper thus pushes the reach of semantics to new domains.
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Kannan, K., Srivastava, B., U.-Sosa, R., Schloss, R.J., Liu, X. (2014). SemEnAl: Using Semantics for Accelerating Environmental Analytical Model Discovery. In: Srinivasa, S., Mehta, S. (eds) Big Data Analytics. BDA 2014. Lecture Notes in Computer Science, vol 8883. Springer, Cham. https://doi.org/10.1007/978-3-319-13820-6_8
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DOI: https://doi.org/10.1007/978-3-319-13820-6_8
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